Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation

Chao, Zhiming; Shi, Danda; Fowmes, Gary John; Xu, Xu; Yue, Wenhan; Cui, Peng; Hu, Tianxiang; Yang, Chuanxin

doi:10.1016/j.geotexmem.2022.10.007

Cited by 24 publications

(4 citation statements)

References 69 publications

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“…Recent related studies include the one by Raja and Shulka [14] in which a new hybrid technique for predicting the settlement of geosynthetic-reinforced soil foundations based on grey wolf optimization (GWO) and artificial neural network (ANN). Most recently, Chao et al [15] published a paper concerning artificial intelligence algorithms for predicting the peak shear strength of clayey soil. These studies have demonstrated that AI also can be used as a design tool when generalized by relevant training data sets.…”

Section: Implications To the Construction Industrymentioning

confidence: 99%

Geosynthetic Solutions for Sustainable Transportation Infrastructure Development

Yoo

2023

Sustainability

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Geosynthetic engineering has made significant advances during the past decade in the areas of manufacturing and practical applications. As a result, geosynthetics have become essential materials that facilitate construction, better improve short- and long-term performance, and reduce long-term maintenance costs in routine civil engineering projects. Geosynthetics are also being recognized as fundamental to sustainable infrastructure development as they reduce the carbon footprint generated by infrastructure development by minimizing the use of natural construction materials. Creative use of geosynthetics in geo-engineering practices is expected to continue to expand as innovative materials and products are becoming available. In this paper, we begin by discussing issues related to climate change. The sustainable benefits of geosynthetics are then presented by demonstrating the potential of geosynthetics to significantly reduce carbon footprints compared to traditional solutions. Finally, recent geosynthetic technologies have been introduced for use in transportation infrastructure. The pathway forward of the geosynthetic technology is also discussed from the view of sustainable infrastructure development.

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Section: Implications To the Construction Industrymentioning

confidence: 99%

Geosynthetic Solutions for Sustainable Transportation Infrastructure Development

Yoo

2023

Sustainability

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“…For example, clayey soils are often adopted as the cover soil of landfills to prevent the contamination of waste to the surrounding environment. During the operational phase of the landfills, drying-wetting cycles may reduce the mechanical strength of the cover soil, leading to the instability of cover system (Chao et al, 2023b;Wang et al, 2022), especially under extreme climatic conditions such as long-term alternating heavy rainfall and drought. Multiple drying-wetting cycles may induce engineering problems including the failure of engineering facilities caused by the weakening hydro-mechanical properties of soil (Shao et al, 2023;Wang et al, 2016;Xu et al, 2022;Zhang et al, 2023a;Zhang et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

Mechanical behaviour of soil under drying–wetting cycles and vertical confining pressures

Chao

Shi

Fowmes

2023

Environmental Geotechnics

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A system for preparing soil specimens subjected to drying-wetting cycles whilst under vertical confining pressures is introduced with Clayey soil specimens subjected to different drying-wetting histories, then the relative performance tested using consolidated undrained triaxial shear tests. Meanwhile, their soil water retention properties were also measured. The experimental results indicate that drying-wetting cycles lead to a rise in the matric suction for soil with high moisture content, and the decrease of matric suction for soil with low moisture content. Partly owing to the higher pore water pressure, peak shear strength reduces gradually during drying-wetting cycles. The impacts of drying-wetting cycles on hydro-mechanical properties of soil specimens during the first cycle are larger than those during the second and the third cycles as the highest matric suction of soil occurs during the first cycle. Vertical confining pressure is shown to limit the impact of drying-wetting cycles on the hydro-mechanical properties of soil effectively because of its restricting effects on the volumetric deformation of soil during the cycles.

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“…Due to the development in computer technology, in recent years, the machine learning techniques are extensively adopted to replicate the complex action mechanism by considering multiple influence factors [ 30 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. In the field of civil engineering, machine learning modeling techniques have found widespread use, including but not limited to the following: estimating rock permeability [ 42 , 43 ]; predicting interface shear strength [ 44 , 45 , 46 ]; assessing cement mortar permeability [ 47 , 48 ]. …”

Section: Introductionmentioning

confidence: 99%

Predicting the Temperature-Dependent Long-Term Creep Mechanical Response of Silica Sand-Textured Geomembrane Interfaces Based on Physical Tests and Machine Learning Techniques

Chao,

Wang,

et al. 2023

Materials

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Preciously assessing the creep mechanical response of sand–geomembrane interfaces is vital for the design of relevant engineering applications, which is inevitable to be influenced by temperature and stress statuses. In this paper, based on the self-developed temperature-controlled large interface shear apparatus, a series of long-term creep shear tests on textured geomembrane–silica sand interfaces in different temperatures, normal pressure, and creep shear pressure were conducted, and a database compiled from the physical creep shear test results is constructed. By adopting the database, three disparate machine learning algorithms of the Back Propagation Artificial Neural Network (BPANN), the Support Vector Machine (SVM) and the Extreme Learning Machine (ELM) were adopted to assess the long-term creep mechanical properties of sand–geomembrane interfaces while also considering the influence of temperature. Then, the forecasting results of the different algorithms was compared and analyzed. Furthermore, by using the optimal machine learning model, sensitivity analysis was carried out. The research indicated that the BPANN model has the best forecasting performance according to the statistics criteria of the Root-Mean-Square Error, the Correlation Coefficient, Wilmot’s Index of Agreement, and the Mean Absolute Percentage Error among the developed models. Temperature is the most important influence factor on the creep interface mechanical properties, followed with time. The research findings can support the operating safety of the related engineering facilities installed with the geomembrane.

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Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation

Cited by 24 publications

References 69 publications

Geosynthetic Solutions for Sustainable Transportation Infrastructure Development

Geosynthetic Solutions for Sustainable Transportation Infrastructure Development

Mechanical behaviour of soil under drying–wetting cycles and vertical confining pressures

Predicting the Temperature-Dependent Long-Term Creep Mechanical Response of Silica Sand-Textured Geomembrane Interfaces Based on Physical Tests and Machine Learning Techniques

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